Luis Herranz

Luis Herranz is a postdoctoral researcher with the Learning and Machine Perception (LAMP) group of the Computer Vision Center and the Universitat Autònoma de Barcelona (UAB). Before, he worked at the Institute of Computing Technology (ICT) of the Chinese Academy of Sciences (CAS) in Beijing, China, and as a researcher for Mitsubishi Electric R&D Centre Europe in Guildford, United Kingdom.

Luis received his PhD from the Universidad Autónoma de Madrid (UAM) in 2010. He has co-authored papers in the main publications in computer vision, artificial intelligence and multimedia, including CVPR, NeurIPS, ICCV, ECCV, AAAI, IJCAI, IEEE TIP, IEEE TMM.

Luis works in machine learning (deep learning in particular) applied to problems in the areas of computer vision and multimedia. He has worked in diverse topics such as video summarization and adaptation, scene recognition, multimodal translation, RGB-D representations, model compression and context-based food recognition. His current research interests include multimodal understanding and communication (image-text, RGB-D), generative models and continual learning.

For more information about recent works, you can check his research blog.

See all Luis Herranz publications here.

SELECTED PUBLICATIONS

• X. Song, S. Jiang, L. Herranz, C. Chen, “Learning Effective RGB-D Representations for Scene Recognition” , IEEE Transactions on Image Processing, vol. 28, no. 2, pp. 980-993, Feb. 2019.

• C. Wu, L. Herranz, X. Liu, Y. Wang, J. van de Weijer, B. Raducanu, “Memory Replay GANs: learning to generate images from new categories without forgetting”, Proc. Neural Information Processing Systems (NeurIPS), Montreal, Canada, Dec. 2018.

• Y. Wang, C. Wu, L. Herranz, J. van de Weijer, A. Gonzalez-García, B. Raducanu, “Transferring GANs: generating images from limited data”, Proc. European Conference on Computer Vision (ECCV18), Munich, Germany, Sept. 2018.

• Y. Wang, J. van de Weijer, L. Herranz, “Mix and match networks: encoder-decoder alignment for zero-pair image translation”, Proc. International Conference on Computer Vision and Pattern Recognition (CVPR18), Salt Lake City, Utah, USA, June 2018.

• M. Masana, J. van de Weijer, L. Herranz, A. D. Bagdanov, J. M. Álvarez, “Domain-adaptive deep network compression”, Proc. International Conference on Computer Vision (ICCV17), Venice, Italy, October 2017.

PUBLICATIONS WITHIN THE P-SPHERE PROJECT

• X. Song, S. Jiang, L. Herranz, C. Chen, “Learning Effective RGB-D Representations for Scene Recognition” , IEEE Transactions on Image Processing, vol. 28, no. 2, pp. 980-993, Feb. 2019.

• C. Wu, L. Herranz, X. Liu, Y. Wang, J. van de Weijer, B. Raducanu, “Memory Replay GANs: learning to generate images from new categories without forgetting”, Proc. Neural Information Processing Systems (NeurIPS), Montreal, Canada, Dec. 2018.

• Y. Wang, C. Wu, L. Herranz, J. van de Weijer, A. Gonzalez-García, B. Raducanu, “Transferring GANs: generating images from limited data”, Proc. European Conference on Computer Vision (ECCV18), Munich, Germany, Sept. 2018.

• O. Caglayan, A. Bardet, F. Bougares, L. Barrault, K. Wang, M. Masana, L. Herranz, J. van de Weijer, “LIUM-CVC Submissions for WMT18 Multimodal Translation Task”, Conference on Machine Translation (WMT’18), October 2018.

• Y. Wang, J. van de Weijer, L. Herranz, “Mix and match networks: encoder-decoder alignment for zero-pair image translation”, Proc. International Conference on Computer Vision and Pattern Recognition (CVPR18), Salt Lake City, Utah, USA, June 2018.

• M. Masana, J. van de Weijer, L. Herranz, A. D. Bagdanov, J. M. Álvarez, “Domain-adaptive deep network compression”, Proc. International Conference on Computer Vision (ICCV17), Venice, Italy, October 2017.

• O. Caglayan, W. Aransa, A. Bardet, M. García-Martínez, F. Bougares, L. Barrault, M. Masana, L. Herranz, J. van de Weijer, “LIUM-CVC Submissions for WMT17 Multimodal Translation Task”, Conference on Machine Translation (WMT’17), Sept. 2017.

• X. Song, S. Jiang, L. Herranz, “Combining Models from Multiple Sources for RGB-D Scene Recognition”, Proc. International Joint Conference on Artificial Intelligence (IJCAI17), Melbourne, Australia, August 2017.